In a computer system, files are typically organized and displayed to a user by showing directories on the user's local storage. Examples of such systems include those running, for example, MICROSOFT® WINDOWS®. To facilitate teamwork over a network or over the Internet, users may store, share and manage files via cloud-based content management systems such as, for example, MICROSOFT® ONEDRIVE™. Such systems make it significantly easier for a user to access this shared content from across a network or the Internet. A user in such an environment has access to a tremendous amount of content, so the user needs to be able to distinguish content relevant to his or her workflow. This may be difficult because relevant content is often scattered across multiple users and stored in multiple places. One potential solution is for a system to use an enterprise graph, such as, for example MICROSOFT® OFFICE 365™ or MICROSOFT® YAMMER®. Using an enterprise graph may allow a user to find relevant content in a quicker, more informed, and efficient manner, but may introduce privacy concerns.
One way to provide information about content and activities within an enterprise is the use of enterprise graphing. In particular, user actions within an enterprise may be stored in various relational data bases (db), log files, etc., and enterprise graphing analyzes these data and interactions to provide the aggregated sum of activity and facts among the enterprise users. In other words, enterprise graphing is an analytical tool to capture and illustrate enterprise data and activity.
An enterprise graph illustrates entities, called “nodes,” and relationships between those entities, called “edges.” More particularly, enterprise graphs illustrate a variety of nodes such as, for example, users, documents, presentations, meetings, emails, etc., and edges define the relationships between those nodes such as authoring, editing, viewing , sharing, sending, etc. In certain circumstances, enterprise graph queries associated with a particular user may return information that is considered private. For example, enterprises may want to restrict access to certain content among its users. Thus, when graph queries are run, the right content must be associated with the right users otherwise private information may be disclosed. However, in order to provide accurate graph data, which may be public, to illustrate the aggregated sum of activity within an enterprise, private data must be considered while ensuring that the privacy of that data is not made public. It is with respect to these and other considerations that the present improvements have been needed.